Custom thread pool in Java 8 parallel stream

Question

Is it possible to specify a custom thread pool for Java 8 parallel stream? I can not find it anywhere.

Imagine that I have a server application and I would like to

Answer 1

The parallel streams use the default ForkJoinPool.commonPool which by default has one less threads as you have processors, as returned by Runtime.getRuntime().availableProcessors() (This means that parallel streams use all your processors because they also use the main thread):

For applications that require separate or custom pools, a ForkJoinPool may be constructed with a given target parallelism level; by default, equal to the number of available processors.

This also means if you have nested parallel streams or multiple parallel streams started concurrently, they will all share the same pool. Advantage: you will never use more than the default (number of available processors). Disadvantage: you may not get "all the processors" assigned to each parallel stream you initiate (if you happen to have more than one). (Apparently you can use a ManagedBlocker to circumvent that.)

To change the way parallel streams are executed, you can either

• submit the parallel stream execution to your own ForkJoinPool: yourFJP.submit(() -> stream.parallel().forEach(soSomething)).get(); or
• you can change the size of the common pool using system properties: System.setProperty("java.util.concurrent.ForkJoinPool.common.parallelism", "20") for a target parallelism of 20 threads. However, this no longer works after the backported patch https://bugs.openjdk.java.net/browse/JDK-8190974.

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Example of the latter on my machine which has 8 processors. If I run the following program:

long start = System.currentTimeMillis();
IntStream s = IntStream.range(0, 20);
//System.setProperty("java.util.concurrent.ForkJoinPool.common.parallelism", "20");
s.parallel().forEach(i -> {
    try { Thread.sleep(100); } catch (Exception ignore) {}
    System.out.print((System.currentTimeMillis() - start) + " ");
});

The output is:

215 216 216 216 216 216 216 216 315 316 316 316 316 316 316 316 415 416 416 416

So you can see that the parallel stream processes 8 items at a time, i.e. it uses 8 threads. However, if I uncomment the commented line, the output is:

215 215 215 215 215 216 216 216 216 216 216 216 216 216 216 216 216 216 216 216

This time, the parallel stream has used 20 threads and all 20 elements in the stream have been processed concurrently.